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一个VLM引导的网络合降解建模,用于降解感知红外和可见图像融合

Jufeng Zhao^1,2, Tianpei Zhang^3,4, Guangmang Cui^1,2

¹Zhejiang Key Laboratory of Optoelectronic Intelligent Imaging and Aerospace Sensing, Hangzhou Dianzi University, Hangzhou, 310018, China.

Scientific reports

|February 11, 2026

概括

这项研究引入了一个新的视觉语言模型引导降解合 (VGDCFusion) 网络,用于红外和可见图像的融合. 通过将降解建模与融合相结合,VGDCFusion有效处理退化图像,优于现有的方法.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
图像处理图像处理

背景情况:

现有的红外和可见图像融合 (IVIF) 方法通常会在损坏的输入时失败,需要手动预处理调整.
在目前的IVIF技术中,降解处理和融合过程的分离导致了大量的性能损失.

研究的目的:

提出一个新的网络,VGDCFusion,它将降解建模直接集成到图像融合过程中.
利用视觉语言模型 (VLMs) 来提高IVIF中的降解意识和指导特征抑制.

主要方法:

开发了特定提示性降解合式提取器 (SPDCE),用于模式特定的降解意识和关节特征提取/抑制.
引入了联合提示降解合 (JPDCF) 系统,用于跨模式降解感知和综合融合与降解过.
利用VLM来指导融合过程,使降解意识感知和抑制成为可能.

主要成果:

在融合退化红外和可见图像方面,VGDCFusion表现出明显的优势.
与最先进的方法相比,在定性视觉质量和定量指标方面取得了实质性的改进 (例如,在AG中~15%,在SF中~14.75%).
拟议的方法通过将其与融合任务相结合,有效地处理图像退化.

结论:

拟议的VGDCFusion网络通过紧密合降解建模和融合,为退化图像融合提供了强大的解决方案.
利用VLM增强了网络感知和抑制退化的能力,从而实现了卓越的融合性能.
该方法代表了IVIF的重大进步,特别是在具有挑战性的,退化输入场景中.

关键词:

深度学习是一种深度学习.红外和可见图像融合.视觉语言模型的模型.

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